Abstract
Large-scale study of glaciers improves our understanding of global glacier change and is imperative for monitoring the ecological environment, preventing disasters, and studying the effects of global climate change. Glaciers in the Hindu Kush Himalaya (HKH) are particularly interesting as the HKH is one of the world's most sensitive regions for climate change. In this work, we: (1) propose a modified version of the U-Net for large-scale, spatially non-overlapping, clean glacial ice, and debris-covered glacial ice segmentation; (2) introduce a novel self-learning boundary-aware loss to improve debris-covered glacial ice segmentation performance; and (3) propose a feature-wise saliency score to understand the contribution of each feature in the multispectral Landsat 7 imagery for glacier mapping. Our results show that the debris-covered glacial ice segmentation model trained using self-learning boundary-aware loss outperformed the model trained using dice loss. Furthermore, we conclude that red, shortwave infrared, and near-infrared bands have the highest contribution toward debris-covered glacial ice segmentation from Landsat 7 images.
Abstract (translated)
大规模研究冰川有助于我们更好地理解全球冰川变化,这对于监测生态环境、防止自然灾害以及研究全球气候变化的影响至关重要。冰川在喜马拉雅山脉(HKH)特别有趣,因为HKH是气候变化最为敏感的地区之一。在本文中,我们:(1)提出了一种规模扩大、空间不重叠、清洁的冰川冰和垃圾覆盖的冰川冰分割的修改版U-Net;(2)引入了一种 novel 的自学习边界意识 loss 来提高垃圾覆盖的冰川冰分割性能;(3)提出了一种特征级别的显著性得分来理解在多光谱 Landsat 7 影像中每个特征对冰川Mapping 的贡献。我们的结果表明,使用自学习边界意识 loss 训练的垃圾覆盖的冰川冰分割模型比使用骰子损失训练的模型表现更好。此外,我们得出结论,红色、短波红外和近红外波段对冰川Mapping 中垃圾覆盖的冰川冰分割贡献最高。
URL
https://arxiv.org/abs/2301.11454
